---
title: "Shim Database Installation With Suspicious Parameters"
excerpt: "Application Shimming
, Event Triggered Execution
"
categories:
  - Endpoint
last_modified_at: 2020-11-23
toc: true
toc_label: ""
tags:
  - Application Shimming
  - Event Triggered Execution
  - Persistence
  - Privilege Escalation
  - Persistence
  - Privilege Escalation
  - Splunk Enterprise
  - Splunk Enterprise Security
  - Splunk Cloud
  - Endpoint
---



[Try in Splunk Security Cloud](https://www.splunk.com/en_us/products/cyber-security.html){: .btn .btn--success}

#### Description

This search detects the process execution and arguments required to silently create a shim database.  The sdbinst.exe application is used to install shim database files (.sdb). A shim is a small library which transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere.

- **Type**: [TTP](https://github.com/splunk/security_content/wiki/Detection-Analytic-Types)
- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: [Endpoint](https://docs.splunk.com/Documentation/CIM/latest/User/Endpoint)- **Datasource**: [Splunk Add-on for Sysmon](https://splunkbase.splunk.com/app/5709)
- **Last Updated**: 2020-11-23
- **Author**: David Dorsey, Splunk
- **ID**: 404620de-46d8-48b6-90cc-8a8d7b0876a3


#### Annotations

<details>
  <summary>ATT&CK</summary>

<div markdown="1">


| ID             | Technique        |  Tactic             |
| -------------- | ---------------- |-------------------- |
| [T1546.011](https://attack.mitre.org/techniques/T1546/011/) | Application Shimming | Persistence, Privilege Escalation |

| [T1546](https://attack.mitre.org/techniques/T1546/) | Event Triggered Execution | Persistence, Privilege Escalation |

</div>
</details>


<details>
  <summary>Kill Chain Phase</summary>

<div markdown="1">

* Actions on Objectives


</div>
</details>


<details>
  <summary>NIST</summary>

<div markdown="1">

* DE.CM



</div>
</details>

<details>
  <summary>CIS20</summary>

<div markdown="1">

* CIS 8



</div>
</details>

<details>
  <summary>CVE</summary>

<div markdown="1">


</div>
</details>

#### Search 

```

| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = sdbinst.exe by Processes.process_name Processes.parent_process_name Processes.dest Processes.user 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `shim_database_installation_with_suspicious_parameters_filter`
```

#### Macros
The SPL above uses the following Macros:
* [security_content_ctime](https://github.com/splunk/security_content/blob/develop/macros/security_content_ctime.yml)
* [security_content_summariesonly](https://github.com/splunk/security_content/blob/develop/macros/security_content_summariesonly.yml)

> :information_source:
> **shim_database_installation_with_suspicious_parameters_filter** is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.

#### Required field
* _time
* Processes.process_name
* Processes.parent_process_name
* Processes.dest
* Processes.user


#### How To Implement
You must be ingesting data that records process activity from your hosts to populate the Endpoint data model in the Processes node. You must also be ingesting logs with both the process name and command line from your endpoints. The command-line arguments are mapped to the "process" field in the Endpoint data model.

#### Known False Positives
None identified

#### Associated Analytic story
* [Windows Persistence Techniques](/stories/windows_persistence_techniques)




#### RBA

| Risk Score  | Impact      | Confidence   | Message      |
| ----------- | ----------- |--------------|--------------|
| 63.0 | 70 | 90 | A process $process_name$ that possible create a shim db silently in host $dest$ |


> :information_source:
> The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author. 

#### Reference


#### Test Dataset
Replay any dataset to Splunk Enterprise by using our [replay.py](https://github.com/splunk/attack_data#using-replaypy) tool or the [UI](https://github.com/splunk/attack_data#using-ui).
Alternatively you can replay a dataset into a [Splunk Attack Range](https://github.com/splunk/attack_range#replay-dumps-into-attack-range-splunk-server)


* [https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.011/atomic_red_team/windows-sysmon.log](https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1546.011/atomic_red_team/windows-sysmon.log)



[*source*](https://github.com/splunk/security_content/tree/develop/detections/endpoint/shim_database_installation_with_suspicious_parameters.yml) \| *version*: **4**